Yeast alleles involved in tolerance to high alcohol levels

ABSTRACT

The present application relates to the field of yeast and, specifically, to the identification of yeast alleles that are involved in maximal alcohol accumulation and/or in tolerance to high alcohol levels. The alcohol may be, e.g., ethanol. The identified alleles can be combined or stacked with each other to construe and/or select high alcohol tolerant yeasts, most notably  Saccharomyces  species.

FIELD OF THE INVENTION

The present application relates to the field of yeast, and specificallyto the identification of yeast alleles that are involved in maximalalcohol accumulation and/or in tolerance to high alcohol levels.Preferably, said alcohol is ethanol. The identified alleles can becombined or stacked with each other to construe and/or select highalcohol tolerant yeasts, most notably Saccharomyces species.

BACKGROUND

The capacity to produce high levels of alcohol is a very rarecharacteristic in nature. It is most prominent in the yeastSaccharomyces cerevisiae, which is able to accumulate in the absence ofcell proliferation, ethanol concentrations in the medium of more than17% (V/V), a level that kills virtually all competing microorganisms. Asa result this property allows this yeast to outcompete all othermicroorganisms in environments rich enough in sugar to sustain theproduction of such high ethanol levels (Casey and Ingledew, 1986;D'Amore and Stewart, 1987). Very few other microorganisms, e.g. theyeast Dekkera bruxellensis, have independently evolved a similar butless pronounced ethanol tolerance compared to S. cerevisiae (Rozpedowskaet al., 2011). The capacity to accumulate high ethanol levels lies atthe basis of the production of nearly all alcoholic beverages as well asbioethanol in industrial fermentations by the yeast S. cerevisiae.Originally, all alcoholic beverages were produced with spontaneousfermentations in which S. cerevisiae gradually increases in abundance,in parallel with the increase in the ethanol level, to finally dominatethe fermentation at the end. The ability to survive and proliferate inhigh levels of ethanol is an ecologically important and industriallyrelevant trait of yeast cells. The ethanol produced by yeast cells slowsdown growth of competing microbes, but at higher concentrations, itcauses stress for the yeast cells themselves. Different yeast strainsshow significant differences in their ability to grow in the presence ofethanol, with the more ethanol-tolerant ones likely having a fitnessadvantage over non-tolerant strains [1-3]. Moreover, ethanol toleranceis a key trait of industrial yeasts that often encounter very highethanol concentrations, for example during beer and wine making andindustrial bio-ethanol production.

The genetic basis of yeast alcohol tolerance, particularly ethanoltolerance has attracted much attention but until recently nearly allresearch was performed with laboratory yeast strains, which display muchlower alcohol tolerance than the natural and industrial yeast strains.This research has pointed to properties like membrane lipid composition,chaperone protein expression and trehalose content, as majorrequirements for ethanol tolerance of laboratory strains (D'Amore andStewart, 1987; Ding et al., 2009) but the role played by these factorsin other genetic backgrounds and in establishing tolerance to very highethanol levels has remained unknown. Different experimental approacheshave been used, including screening of (deletion) mutants for increasedethanol tolerance, transcriptome analysis of ethanol stressed cells andQTL analyses aimed at identifying mutations that cause differences inethanol tolerance between different yeast strains [4-10]. A polygenicanalysis of the high ethanol tolerance of a Brazilian bioethanolproduction strain VR1 revealed the involvement of several genespreviously never connected to ethanol tolerance and did not identifygenes affecting properties classically considered to be required forethanol tolerance in lab strains (Swinnen et al., 2012a). Together,these studies have linked multiple different genetic loci to ethanoltolerance and identified hundreds of genes, involved in a multitude ofcellular processes [11-14].

A shortcoming of most previous studies is the assessment of alcoholtolerance solely by measuring growth on nutrient plates in the presenceof increasing alcohol levels. (D'Amore and Stewart, 1987; Ding et al.,2009). This is a convenient assay, which allows hundreds of strains orsegregants to be phenotyped simultaneously with little work andmanpower. However, a more physiologically and ecologically relevantparameter of alcohol tolerance in S. cerevisiae is its capacity toaccumulate by fermentation high alcohol levels in the absence of cellproliferation. This generally happens in an environment with a largeexcess of sugar compared to other essential nutrients. As a result, alarge part of the alcohol in a typical, natural or industrial, yeastfermentation is produced with stationary phase cells in the absence ofany cell proliferation. The alcohol tolerance of the yeast under suchconditions determines its maximal alcohol accumulation capacity, aspecific property of high ecological and industrial importance. Inindustrial fermentations, a higher maximal alcohol accumulation capacityallows a better attenuation of the residual sugar and therefore resultsin a higher yield. A higher final alcohol titer reduces the distillationcosts and also lowers the liquid volumes in the factory, which hasmultiple beneficial effects on costs of heating, cooling, pumping andtransport of liquid residue. It also lowers microbial contamination andthe higher alcohol tolerance of the yeast generally also enhances therate of fermentation especially in the later stages of the fermentationprocess. Maximal alcohol accumulation capacity can only be determined inindividual yeast fermentations, which are much more laborious to performthan growth tests on plates. In static industrial fermentations,maintenance of the yeast in suspension is due to the strong CO₂ bubblingand this can only be mimicked in lab scale with a sufficient amount ofcells in a sufficiently large volume.

While it becomes increasingly clear that ethanol is a complex stressthat acts on several different processes including increasing fluidityand permeability of cellular membranes, changing activity and solubilityof membrane-bound and cytosolic proteins and interfering with the protonmotive force (for review, see [15-17], the exact molecular mechanismsand genetic architecture underlying ethanol tolerance are still largelyunknown.

Experimental evolution to study adaptation to increased ethanol levelscould provide more insight into the molecular mechanisms underlyingethanol tolerance since such experiments could reveal differentmutational paths that make a sensitive strain more tolerant. Only ahandful of studies have looked at adaptation to ethanol in originallynon-ethanol tolerant microbes exposed to gradually increasing levels ofethanol [6,18-22]. These have mostly focused on the physiologicaladaptations found in the evolved cells and have not performed anextensive analysis of the mechanisms and genetic changes underlying thisadaptation. Hence, a comprehensive analysis of the type and number ofmutations a non-ethanol tolerant strain can (or needs to) acquire tobecome more ethanol tolerant is still lacking. Experimental evolutionhas proven to be a valuable tool to investigate the different mechanismsand pathways important for cells to adapt to specific selectiveconditions. Seminal papers have increased our understanding of themolecular basis of adaptation to specific stresses, such as heat stress,nutrient limitation and antibiotic treatment [23-28]. Recent advances inDNA sequencing technologies allow affordable and fast sequencing ofcomplete genomes of clones and populations. While sequencing clonesyields information on individual lineages within the experiment,population data provides information on the heterogeneity of adaptation.Additionally, sequencing samples isolated at different time pointsduring the evolution experiment makes it possible to capture evolutionin action. This has provided valuable information on the rate and typesof mutations underlying adaptation, the genetic basis of ‘novel’phenotypes and the existence of parallel pathways to establishcomparable phenotypic outcomes [29-32]. For example, a common strategyobserved in populations evolving under nutrient limitation is theamplification of genetic regions encoding transporters responsible forthe uptake of the limiting nutrient [24,33]. Other studies usingmultiple replicate populations have discovered a high degree ofparallelism in the adaptive solutions found by different populations.Clonal interference, the competition between lineages carrying differentbeneficial mutations, is another commonly observed phenomenon inevolution of asexually propagating populations that can increasecomplexity of mutational dynamics as well as impede the spread ofbeneficial mutations in a population [32,34,35]. To unravel themolecular mechanisms of adaptation to a specific condition, most studieshave used isogenic replicate populations, with all cells having the sameinitial genome size. Genome size can significantly change duringevolution; with both small-scale changes (chromosomal 108 deletions andamplifications) and large-scale changes (increase or decrease inploidy). Moreover, ploidy shifts have been reported in the evolutionaryhistory of many organisms, including Saccharomyces cerevisiae and as aresponse to selective pressure [36-38]. Conversely, genome size has alsobeen reported to affect evolution rate: polyploidy has been shown toincrease adaptability [39,40]. Multiplying the amount of DNA increasesthe genetic material available for evolution to tinker with and canalter gene expression [41-43]. These polyploid genomes can be unstable,resulting in loss of chromosomes and thus aneuploid cells [44-46].Although studies have looked at adaptation of lineages of differentploidy, none have followed the mutational dynamics in these evolvingpopulations over time in detail [36,47-49].

It would be advantageous to identify novel pathways and alleles thatcontribute to alcohol tolerance and are relevant in industrial yeaststrains as well, to further increase yield of alcohol fermentations withsuch strains. Indeed, even a slight increase in alcohol tolerance oralcohol accumulation capacity would have huge economic benefits.

SUMMARY

Experimental evolution of isogenic yeast populations of differentinitial ploidy was used to study adaptation to increasing levels ofethanol. Evolved lineages showed a significant increase in ethanoltolerance compared to ancestral strains. High coverage whole-genomesequencing of more than 30 evolved populations and over 100 adaptedclones isolated throughout a two-year evolution experiment revealed howa complex interplay of different evolutionary mechanisms led to highertolerance, including de novo single nucleotide mutations, extensive copynumber variation and ploidy changes. Although the specific mutationsdiffer between different evolved lineages, application of a novelcomputational pipeline to identify target pathways reveals shared themesat the level of functional modules. Moreover, by combining an allelicreplacement approach with high-throughput fitness measurements, we couldidentify several SNPs that arose in our adapted cells previously notimplicated in ethanol tolerance and that significantly increased ethanoltolerance when introduced into a non-tolerant background. Takentogether, our results show how, in contrast to adaptation to some otherstresses, adaptation to a complex and severe stress involves aninterplay of different evolutionary mechanisms. In addition, our studyhighlights the potential of experimental evolution to identify mutationsthat are of industrial importance.

It is an object of the invention to provide alleles that increasealcohol tolerance and/or alcohol accumulation in yeast. Mostparticularly, the alcohol is ethanol. Most particularly, the yeast is aSaccharomyces species. Increase in alcohol tolerance is an economicallyrelevant property, as a non-limiting example, an increase in alcoholtolerance and/or accumulation may be favourable for bio-ethanolproduction. The increase in alcohol tolerance and/or accumulation mayresult in a higher speed of fermentation (i.e. less time needed to reacha particular % of alcohol) and/or a higher final ethanol titer.

According to specific embodiments, such alleles are selected from aMEX67 allele, a PCA1 allele, a PRT1 allele, a YBL059W allele, a HEM13allele, a HST4 allele, a VPS70 allele. The alleles can also be inintergenic regions. According to these embodiments, the alleles aretypically selected from the intergenic region of Chromosome IV(particularly around or at position 1489310) and the intergenic regionof Chromosome XII (particularly around or at position 747403).

Particularly envisaged alleles are selected from the group consisting ofa MEX67 allele with a G456A mutation, PCA1 with a C1583T mutation, PRT1with a A1384G mutation, YBL059W with a G479T mutation, HEM13 with aG700C mutation, HST4 with a G262C mutation, VPS70 with a C595A mutation,the intergenic region of Chromosome IV with a A>T substitution atposition 1489310, and the intergenic region of Chromosome XII with a C>Tsubstitution at position 747403. It is particularly envisaged that thewild type alleles mentioned are selected from MEX67 as shown in SEQ IDNO: 1, PCA1 as shown in SEQ ID NO: 2, PRT1 as shown in SEQ ID NO: 3,YBL059W as shown in SEQ ID NO:4, HEM13 as shown in SEQ ID NO:5, HST4 asshown in SEQ ID NO:6, and VPS70 as shown in SEQ ID NO:7. Thus, it isparticularly envisaged that the allele is selected from the groupconsisting of SEQ ID NO:1 with a G456A mutation, SEQ ID NO:2 with aC1583T mutation, SEQ ID NO:3 with a A1384G mutation, SEQ ID NO:4 with aG479T mutation, SEQ ID NO:5 with a G700C mutation, SEQ ID NO:6 with aG262C mutation, or SEQ ID NO:7 with a C595A mutation.

The alleles can be used to increase alcohol tolerance and/or alcoholaccumulation in yeast either alone or in combination. The latterprovides additive or even synergistic effects. Anypermutation/combination of the 9 alleles can be used to increase alcoholtolerance and/or alcohol accumulation, and this is explicitly envisagedherein. Any combination of these alleles (including single alleles) mayalso be combined with known mutations or alleles that increase alcoholtolerance and/or alcohol accumulation. This is particularly the casewhen the alleles are to be used in industrial yeast strains adapted tohave high alcohol tolerance.

Particularly envisaged combinations are those with the MEX67 allele.According to these embodiments, a MEX67 allele is provided to increasealcohol tolerance and/or alcohol accumulation in yeast. This MEX67allele may be combined with other alcohol tolerance and/or accumulationmodulating alleles. This may be known alcohol tolerance alleles. It isalso explicitly foreseen that the MEX67 allele may be incorporated in anindustrial yeast strain adapted to have high alcohol tolerance (and thatthus already has a combination of known alcohol tolerance alleles).However, it is also explicity envisaged that the MEX67 allele is furthercombined with the new alcohol tolerance and/or accumulation modulatingalleles reported herein. According to these embodiments, the use of aMEX67 allele is provided, wherein said MEX67 allele is combined withother alcohol tolerance and/or accumulation modulating alleles.Particularly, said other alcohol tolerance and/or accumulationmodulating alleles are selected from the group consisting of PCA1, PRT1,YBL059W, HEM13, HST4, and VPS70. According to alternative embodiments,they may also be selected from the intergenic region of Chromosome IV(particularly around or at position 1489310) and the intergenic regionof Chromosome XII (particularly around or at position 747403). Accordingto specific embodiments, the MEX67 allele has a G456A mutation.According to further specific embodiments, the other alcohol toleranceand/or accumulation modulating alleles are selected from the groupconsisting of PCA1 with a C1583T mutation, PRT1 with a A1384G mutation,YBL059W with a G479T mutation, HEM13 with a G700C mutation, HST4 with aG262C mutation, VPS70 with a C595A mutation, the intergenic region ofChromosome IV with a A>T substitution at position 1489310, and theintergenic region of Chromosome XII with a C>T substitution at position747403.

Albeit that the combinations with the MEX67 alleles are particularlyenvisaged, the above applies mutatis mutandis to the other allelesdescribed herein (e.g. the PCA1, PRT1, YBL059W, . . . alleles).

The alleles described herein have not before been linked to increasedalcohol tolerance or accumulation. This is also true for the genes andintergenic regions, with exception of VPS70. For VPS70, another allelehas recently been shown to also modulate alcohol tolerance(WO2014/170330). Thus, according to particular embodiments, VPS70 isexcluded from the envisaged combinations. According to alternativeembodiments, when a VPS70 allele is used to increase alcohol toleranceand/or accumulation, it is the VPS70 allele with a C595A mutation.

According to a further aspect, the alleles provided herein, particularlythe MEX67 allele, are used for selecting a yeast strain with higheralcohol accumulation and/or resistance. Particularly, said yeast is aSaccharomyces spp. Particularly, the alcohol is ethanol.

The selection of the strain can be carried out with every method knownto the person skilled in the art. As a non-limiting example, strains maybe selected on the base of an identification of the allele by PCR orhybridization. The selection may be combined by a selection for otheralleles, known to be involved in alcohol accumulation and/or alcoholtolerance, such as but not limited to specific alleles of ADE1, VPS70,MKT1, APJ1, SWS2, or KIN3. Said selection may be carried outsimultaneously or consecutively. In case of a consecutive selection thesequence of the selection is not important, i.e. the selection usingMEX67 may be carried out before or after the other selection rounds.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1. Experimental setup.

(A) Experimental evolution of prototrophic, isogenic populations ofdifferent ploidy (haploid (VK111), diploid (VK145) and tetraploid(VK202)) for increased ethanol tolerance was performed in a turbidostat.Every 25 generations, the ethanol concentration in the media wasincreased in a stepwise manner (starting at 6% (v/v) and reaching 12% at200 generations). Increasing the ethanol concentration from 10% to 11%dramatically reduced growth rate of evolving cells. Therefore, insteadof increasing ethanol levels, we first reduced the ethanol level to10.7% after 100 generations. (B) Red circles represent sampling points(indicated as number of generations) for which whole-genome sequencingwas performed. For each circle, heterogeneous populations as well asthree evolved, ethanol tolerant clones were sequenced. Sequencing of thepopulation sample of reactor 4 at 200 generations failed, so this datais omitted from the specification. For generation 80 of reactor 1, onlypopulation data is available.

FIG. 2. Evolved populations are more ethanol tolerant.

Evolved populations from different reactors show increased fitness inEtOH. Fitness was determined for population samples of each reactorafter 40 generations (blue) and 200 generations (red). Fitness isexpressed relative to the ancestral strain of each reactor (haploid forreactor 1-2; diploid for reactor 3-4 and tetraploid for reactor 5-6).Data represent the average of three independent measurements, error barsrepresent standard deviation.

FIG. 3. Haploid lineages diploidized during adaptation to EtOH.

Flow cytometry analysis of DNA content (stained by propidium iodide) ofevolved populations, compared to ancestral haploid (red) and diploid(blue). For the clonal ancestral samples, two peaks are observed,corresponding to the G1 and G2 phase of the cell cycle. Evolvedpopulations sometimes display three peaks, indicative of both haploidand diploid subpopulations.

FIG. 4. Mutations present in evolved clones isolated from all reactorsat 200 generations. Circle diagrams depict the number and types ofmutations identified in evolved clones at 200 generations of reactor 1,reactor 2, reactor 3, reactor 4, reactor 5 and reactor 6.

FIG. 5. Copy number variation in evolved clones of reactor 2 and 3isolated at 200 generations.

Genome view of yeast chromosomes and CNV patterns from a sliding windowanalysis. The y axis represents log 2 ratios of the coverage observedacross 500 bp genomic windows to the coverage expected in a diploidgenome without CNVs. Area of the plot located between the red lines(from −0.23 to −1) marks putative CNV loss events, whereas regionbetween the blue lines 270 (from 0.23 to 0.58) marks putative CNV gainevents. It should be noted that the amplified region of chromosome XIIobserved in some of our clones does not correspond to the ribosomal DNAgenes.

FIG. 6. Dynamics and linkage of mutations in evolved populations ofreactor 2.

Mutations (reaching a frequency of least 20% in the evolved populationsamples) and corresponding frequencies were identified from populationsequencing data. Muller diagram represents the hierarchical clusteringof these mutations, with each color block representing a specific groupof linked mutations. Indels are designated with I, whereas heterozygousmutations are in italics. Mutations present as heterozygous mutations inall clones of a specific time point and present at a frequency of 50% inthe population, are depicted as a frequency of 100% in the population,since it is expected that all cells in the population contain thismutation. After 80 generations, a mutator phenotype appeared in thisreactor (indicated by arrow under graph), which coincides with the risein frequency of an indel in the mismatch repair gene MSH2. Frequenciesof haplotypes, dynamics and linkage for reactor 1 were calculated butare not shown.

FIG. 7. Adaptive pathways are involved in cell cycle, DNA repair andprotoporphyrinogen metabolism.

Shown is the sub-network that prioritizes putative adaptive mutations byapplying PheNetic on all selected mutations, excluding those originatingfrom the populations with a mutator phenotype i.e. reactor 2 and 6. Thenodes in the network correspond to genes and/or their associated geneproducts. Node borders are colored according to the reactors containingthe populations in which these genes were mutated. Nodes are coloredaccording to gene function, for each gene the most enriched term isvisualized (grey indicates no enrichment). Cell cycle related processeshave been subdivided into DNA replication and interphase. The edgecolors indicate different interaction types. Orange lines representmetabolic interactions, green lines represent protein-proteininteractions, red lines represent protein-DNA interactions. Sub-networkswere extracted by separately analyzing the mutated genes observed ineach of the different populations (reactors)—this data is not shownhere.

FIG. 8. Single mutations present in evolved populations can increaseethanol tolerance of a non-adapted strain.

Plots show the average selection coefficient (smut) as a function ofethanol concentration for (A) pca1^(C1583T), (B) prt1^(A1384G), (C)ybl059w^(G479T), (D) intergenic ChrIV A1489310T, (E) hem13^(G700C), (F)intergenic ChrXII C747403T, (G) hst4^(G262C), (H) vps70^(C595A), and (I)mex67^(G456A). Superscripts denote the exact nucleotide change in eachof the mutants tested. YECitrine-tagged mutants were competed with themCherry-tagged parental strain (orange dots); dye-swap experiments werecarried out by competing the mCherry-tagged mutants with the YECitrineparental strain (blue dots), except for (I). Error bars show the S.E.M.from three experimental replicates. Asterisk show P-values from theone-way ANOVA tests of the mean differences in 4-8% ethanol compared tofitness in 0% ethanol: * p<0.05; ** p<0.01; ***p<0.005.

FIG. 9. Mutations identified in lab strains also increase alcoholtolerance in an industrial reference strain.

Coding mutations were introduced into the industrial reference biofuelstrain Ethanol Red. Ethanol production of these mutants is shown,relative to its proper control. All experiments were repeated 4 times(real biological replicates, using independent transformants whereavailable).

FIG. 10. Fermentation performance of mutant strains is comparable towild type strain.

Coding mutations were introduced into the industrial reference biofuelstrain Ethanol Red. Fermentation performance in YP+35% glucose (w/v) isshown as the cumulative weight loss—a proxy for CO₂ production—duringthe fermentation. Each line represents the average of 4 replicatefermentations, error bars represent standard deviation.

DETAILED DESCRIPTION Definitions

The present invention will be described with respect to particularembodiments and with reference to certain drawings but the invention isnot limited thereto but only by the claims. Any reference signs in theclaims shall not be construed as limiting the scope. The drawingsdescribed are only schematic and are non-limiting. In the drawings, thesize of some of the elements may be exaggerated and not drawn on scalefor illustrative purposes. Where the term “comprising” is used in thepresent description and claims, it does not exclude other elements orsteps. Where an indefinite or definite article is used when referring toa singular noun e.g. “a” or “an”, “the”, this includes a plural of thatnoun unless something else is specifically stated.

Furthermore, the terms first, second, third and the like in thedescription and in the claims, are used for distinguishing betweensimilar elements and not necessarily for describing a sequential orchronological order. It is to be understood that the terms so used areinterchangeable under appropriate circumstances and that the embodimentsof the invention described herein are capable of operation in othersequences than described or illustrated herein.

The following terms or definitions are provided solely to aid in theunderstanding of the invention. Unless specifically defined herein, allterms used herein have the same meaning as they would to one skilled inthe art of the present invention. Practitioners are particularlydirected to Sambrook et al., Molecular Cloning: A Laboratory Manual,2^(nd) ed., Cold Spring Harbor Press, Plainsview, N.Y. (1989); andAusubel et al., Current Protocols in Molecular Biology (Supplement 47),John Wiley & Sons, New York (1999), for definitions and terms of theart. The definitions provided herein should not be construed to have ascope less than understood by a person of ordinary skill in the art.

An allele as used here is a specific form of the gene, which is carryingSNP's or other mutations, either in the coding (reading frame) or thenon-coding (promoter region, or 5′ or 3′ non-translated end) part of thegene, wherein said mutations distinguish the specific form from otherforms of the gene.

Gene as used here includes both the promoter and terminator region ofthe gene as well as the coding sequence. It refers both to the genomicsequence (including possible introns) as well as to the cDNA derivedfrom the spliced messenger, operably linked to a promoter sequence.

Coding sequence is a nucleotide sequence, which is transcribed into mRNAand/or translated into a polypeptide when placed under the control ofappropriate regulatory sequences. The boundaries of the coding sequenceare determined by a translation start codon at the 5′-terminus and atranslation stop codon at the 3′-terminus. A coding sequence caninclude, but is not limited to mRNA, cDNA, recombinant nucleotidesequences or genomic DNA, while introns may be present as well undercertain circumstances.

Promoter region of a gene as used here refers to a functional DNAsequence unit that, when operably linked to a coding sequence andpossibly a terminator sequence, as well as possibly placed in theappropriate inducing conditions, is sufficient to promote transcriptionof said coding sequence

Nucleotide sequence”, “DNA sequence” or “nucleic acid molecule(s)” asused herein refers to a polymeric form of nucleotides of any length,either ribonucleotides or deoxyribonucleotides. This term refers only tothe primary structure of the molecule. Thus, this term includes double-and single-stranded DNA, and RNA. It also includes known types ofmodifications, for example, methylation, “caps” substitution of one ormore of the naturally occurring nucleotides with an analog.

Modulation of alcohol accumulation and/or tolerance, as used here, meansan increase or a decrease of the alcohol concentration, produced by theyeast carrying the specific allele, as compared with the alcoholconcentration produced under identical conditions by a yeast that isgenetically identical, apart from the specific allele(s)

Alcohol as used here can be any kind of alcohol, including, but notlimited to methanol, ethanol, n- and isopropanol, n- and isobutanol.Indeed, several publications indicate that the tolerance to ethanol andother alkanols is determined by the same mechanisms (Carlsen et al.,1991; Casal et al., 1998).

The causes and mechanisms underlying evolutionary adaptation are keyissues in biology. However, the mechanisms underlying evolutionaryadaptation to complex and severe stress factors (e.g. broadly actingtoxins or extreme niches that require immediate, complex adaptivechanges) remain understudied. Here, we study how yeast populations adaptto gradually increasing ethanol levels—a broad-acting, ecologically andindustrially relevant complex stress that is still poorly understood.Our results reveal how over a two-year evolution period, severalevolutionary mechanisms—including mutator phenotypes, changes in ploidy,and complex clonal interference—result in adapted populations capable ofsurviving in medium containing up to 12% ethanol. In addition, we reportseveral previously unknown adaptive mutations that increase ethanolresistance; which may open new routes to increase the efficiency ofindustrial fermentations. Specifically, we exposed six initiallyisogenic yeast populations of different ploidy to increasing levels ofethanol. We combined high-coverage whole-genome sequencing of more than30 populations and 100 clones isolated throughout this two-yearevolution experiment with a novel computational pipeline to reveal themutational dynamics, molecular mechanisms and networks underlyingincreased ethanol tolerance of our evolved lineages. High-throughputfitness measurements allowed us to characterize the phenotypic effect ofidentified mutations in different environments. Our results suggest thatadaptation to high ethanol is complex and can be reached throughdifferent mutational pathways. We find that adaptation to high ethanollevels involves the appearance of mutator phenotypes, and evolvingpopulations showing strong clonal interference. Evolved cells displayextensive variation in genome size; with initially haploid andtetraploid populations showing quick convergence to a diploid state. Ourresults are the first to attribute a significant fitness advantage of adiploid cell over an isogenic haploid cell under selective conditions.In addition, evolved clones repeatedly gained extra copies of the samechromosomes. By combining an allelic replacement approach withhigh-throughput fitness measurements, we could identify severalmutations previously not implicated in ethanol tolerance thatsignificantly increased ethanol tolerance when introduced into anon-tolerant background. Together, our study yields a detailed view ofthe molecular evolutionary processes and genetic changes underlyinglong-term adaptation to a severe and complex stress, and highlights thepotential of experimental evolution to identify mutations that are ofindustrial importance. The methods developed and insights gained serveas a model for adaptation to a complex and lethal stress. Moreover,specific adaptive SNPs can be used to guide engineering strategies aimedat obtaining superior biofuel yeasts. Last but not least, ourexperimental setup is also interesting because it takes an atypicalapproach. In many experimental evolution studies, populations areexposed for a limited amount of time (typically a few months) to adefined, constant and ‘simple’ stress (e.g. a fixed concentration ofantibiotic or limiting nutrient), so that in the beginning of theexperiment, cells are confronted with a very strong selection, while theselection pressure is reduced or even eliminated when cells becomeresistant. In our experimental set-up, ethanol levels were graduallyincreased over time so that cells were evolving under constant selectionover a long (two-year) period. High concentrations of ethanol slow downgrowth and actively kill non-tolerant cells. Our work thus combinesaspects of traditional evolution studies with principles used inso-called morbidostat experiments, and may therefore offer a realisticpicture of adaptation to a complex and severe stress where a populationgradually penetrates a new niche.

It is an object of the invention to provide alleles that increasealcohol tolerance and/or alcohol accumulation in yeast. Mostparticularly, the alcohol is ethanol. Most particularly, the yeast is aSaccharomyces species.

According to specific embodiments, such alleles are selected from aMEX67 allele, a PCA1 allele, a PRT1 allele, a YBL059W allele, a HEM13allele, a HST4 allele, a VPS70 allele. The alleles can also be inintergenic regions. According to these embodiments, the alleles aretypically selected from the intergenic region of Chromosome IV(particularly around or at position 1489310) and the intergenic regionof Chromosome XII (particularly around or at position 747403).

Particularly envisaged alleles are selected from the group consisting ofa MEX67 allele with a G456A mutation, PCA1 with a C1583T mutation, PRT1with a A1384G mutation, YBL059W with a G479T mutation, HEM13 with aG700C mutation, HST4 with a G262C mutation, VPS70 with a C595A mutation,the intergenic region of Chromosome IV with a A>T substitution atposition 1489310, and the intergenic region of Chromosome XII with a C>Tsubstitution at position 747403.

The alleles can be used to increase alcohol tolerance and/or alcoholaccumulation in yeast either alone or in combination. The latterprovides additive or even synergistic effects. Anypermutation/combination of the 9 alleles can be used to increase alcoholtolerance and/or alcohol accumulation, and this is explicitly envisagedherein. Any combination of these alleles (including single alleles) mayalso be combined with known mutations or alleles that increase alcoholtolerance and/or alcohol accumulation. This is particularly the casewhen the alleles are to be used in industrial yeast strains adapted tohave high alcohol tolerance.

Particularly envisaged combinations are those with the MEX67 allele.According to these embodiments, a MEX67 allele is provided to increasealcohol tolerance and/or alcohol accumulation in yeast. This MEX67allele may be combined with other alcohol tolerance and/or accumulationmodulating alleles. This may be known alcohol tolerance alleles. It isalso explicitly foreseen that the MEX67 allele may be incorporated in anindustrial yeast strain adapted to have high alcohol tolerance (and thatthus already has a combination of known alcohol tolerance alleles).Non-limiting examples include ADE1, INO1, VPS70, MKT1, APJ1, SWS2, PDR1,or KIN3.

However, it is also explicitly envisaged that the MEX67 allele isfurther combined with the new alcohol tolerance and/or accumulationmodulating alleles reported herein. According to these embodiments, theuse of a MEX67 allele is provided, wherein said MEX67 allele is combinedwith other alcohol tolerance and/or accumulation modulating alleles.Particularly, said other alcohol tolerance and/or accumulationmodulating alleles are selected from the group consisting of PCA1, PRT1,YBL059W, HEM13, HST4, and VPS70. According to alternative embodiments,they may also be selected from the intergenic region of Chromosome IV(particularly around or at position 1489310) and the intergenic regionof Chromosome XII (particularly around or at position 747403). Accordingto specific embodiments, the MEX67 allele has a G456A mutation.According to further specific embodiments, the other alcohol toleranceand/or accumulation modulating alleles are selected from the groupconsisting of PCA1 with a C1583T mutation, PRT1 with a A1384G mutation,YBL059W with a G479T mutation, HEM13 with a G700C mutation, HST4 with aG262C mutation, VPS70 with a C595A mutation, the intergenic region ofChromosome IV with a A>T substitution at position 1489310, and theintergenic region of Chromosome XII with a C>T substitution at position747403.

Albeit that the combinations with the MEX67 alleles are particularlyenvisaged, the above applies mutatis mutandis to the other allelesdescribed herein (e.g. the PCA1, PRT1, YBL059W, . . . alleles).

The alleles described herein have not before been linked to increasedalcohol tolerance or accumulation. This is also true for the genes andintergenic regions, with exception of VPS70. For VPS70, another allelehas recently been shown to also modulate alcohol tolerance. Thus,according to particular embodiments, VPS70 is excluded from theenvisaged combinations. According to alternative embodiments, when aVPS70 allele is used to increase alcohol tolerance and/or accumulation,it is the VPS70 allele with a C595A mutation

According to a further aspect, the alleles provided herein, particularlythe MEX67 allele, are used for selecting a yeast strain with higheralcohol accumulation and/or resistance. Particularly, said yeast is aSaccharomyces spp. Particularly, the alcohol is ethanol.

In a specific embodiment the invention provides a yeast strain,particularly an industrial yeast strain, particularly an industrialyeast strain of Saccharomyces cerevisiae comprising at least one alleleselected from the group consisting of a MEX67 allele with a G456Amutation, PCA1 with a C1583T mutation, PRT1 with a A1384G mutation,YBL059W with a G479T mutation, HEM13 with a G700C mutation, HST4 with aG262C mutation, VPS70 with a C595A mutation, the intergenic region ofChromosome IV with a A>T substitution at position 1489310, and theintergenic region of Chromosome XII with a C>T substitution at position747403.

The selection of the strain can be carried out with every method knownto the person skilled in the art. As a non-limiting example, strains maybe selected on the base of an identification of the allele by PCR orhybridization. The selection may be combined by a selection for otheralleles, known to be involved in alcohol accumulation and/or alcoholtolerance, such as but not limited to specific alleles of ADE1, VPS70,MKT1, APJ1, SWS2, or KIN3. Said selection may be carried outsimultaneously or consecutively. In case of a consecutive selection thesequence of the selection is not important, i.e. the selection usingMEX67 may be carried out before or after the other selection rounds.

It is to be understood that although particular embodiments, specificconfigurations as well as materials and/or molecules, have beendiscussed herein for cells and methods according to the presentinvention, various changes or modifications in form and detail may bemade without departing from the scope and spirit of this invention. Thefollowing examples are provided to better illustrate particularembodiments, and they should not be considered limiting the application.The application is limited only by the claims.

EXAMPLES Example 1. Experimental Evolution to Increase Alcohol ToleranceIntroduction

In this study, we use experimental evolution to dissect the adaptivemechanisms underlying ethanol tolerance in yeast. Six isogenicSaccharomyces cerevisiae populations of different ploidy (haploid,diploid and tetraploid) were asexually propagated in a turbidostat overa two year period, with ethanol levels gradually increasing during theexperiment. This step-wise increase in exogenous ethanol levels resultedin a constant selective pressure for our cells. Whole-genome sequencingof evolved populations and isolated, ethanol-tolerant clones atdifferent times during the experiment allowed us to paint a detailedpicture of the mutational dynamics in the different populations. Severalcommon themes in the type of adaptations and evolutionary mechanismsemerge, with all lines showing extensive clonal interference as well ascopy number variations. Additionally, the haploid and tetraploid linesshowed rapid convergence towards a diploid state. Despite these commonthemes, we find multiple lineage-specific adaptations, with littleoverlap in the mutated genes between the different populations. Byapplying a novel computational pipeline to identify affected pathways,we could reveal overlap between the functional modules affected in thedifferent adapted populations, with both novel and previouslyestablished pathways and genes contributing to ethanol tolerance.Importantly, introduction of specific mutated alleles present in adaptedpopulations into the ancestral strain significantly increased itsethanol tolerance, demonstrating the adaptive nature of these mutationsand the potential of using experimental evolution to unravel and improvea complex phenotype.

1.1 Evolved Cells are More Ethanol Tolerant

To study the mutational dynamics underlying increased ethanol tolerance,six prototrophic, isogenic S. cerevisiae strains of different ploidy(two haploid, two diploid and two tetraploid lines—with each line of thesame initial ploidy started from the same preculture (VK111, VK145 andVK202 respectively) were subjected to increasing levels of ethanol. Thiswas done by gradually increasing ethanol levels from 6% (v/v) to 12%over a two-year period in a continuous turbidostat with glucose (4%(w/v)) as a carbon source. Samples were taken at regular time intervalsand subjected to whole-genome sequencing analysis. In addition tosequencing each of the 6 evolving populations, we also sequenced thegenomes of three clones isolated from each of the population samples,resulting in a total of 34 population samples and 102 clonal samplesthat were sequenced. FIG. 1 depicts the experimental set-up as well asthe time points (number of generations) for which whole-genomesequencing was performed.

We determined the relative fitness of the evolved populations, isolatedat 40 and 200 generations, and generally observed increases in fitnessin high (9% v/v) EtOH (FIG. 2 & Materials and Methods). In general,clones taken from the same population at the same time point had similarfitness; with some notable exceptions suggesting considerableheterogeneity within populations (data not shown). These fitnessmeasurements were performed in 9% ethanol because the ancestralreference strains did not grow at higher ethanol levels. It is importantto note that the actual increase in fitness of our evolved strains inhigher ethanol levels (above 9% (v/v)) is much larger than what can beappreciated from FIG. 2. Spotting of our evolved lineages on agar plateswith increasing levels of ethanol indicates that these adapted strainscan grow on concentrations up to 11% (v/v) EtOH, with the controlancestral strains showing almost no growth under these conditions (i.e.an infinite increase in fitness compared to the ancestral strain) (datanot shown).

1.2 Diploidization of Haploid Cells is a Fast Way of Increasing EthanolTolerance

Propidium iodide staining and flow cytometry analysis of evolvedpopulations showed that diploid cells appeared relatively quickly in theoriginally haploid populations (FIG. 3). This was observed independentlyin both reactors started from the same isogenic haploid population. Inboth reactors, diploid cells have taken over the entire population by130 generations. These diploid variants are still of mating type a, thesame as the initial haploid ancestral strain, indicating that they didnot arise through mating type switching and subsequent mating. Ourresults indicate that the diploids in our turbidostat are likely theresult of failed cytokinesis. The relatively rapid evolutionary sweep ofthe new diploid variants points to a significant selective advantage.Competition experiments confirmed that the fitness of a diploid cell isindeed significantly higher than that of an isogenic haploid strain in9% EtOH (p=0.0063; unpaired t-test), whereas there is no significantfitness difference in the absence of ethanol (p=0.469; unpaired t-test).(data not shown). Interestingly, our tetraploid starting populationsalso converged to a diploid state relatively fast during the evolutionexperiment. The parallel diploidization observed in two reactors withhaploid ancestral cells and two reactors with tetraploid ancestral cellsmight suggest that becoming diploid is one of the main and/or one of themore easily accessible routes leading to increased ethanol tolerance.

Whole-Genome Sequencing of Evolved Populations and Clones Reveals aComplex Pattern of Adaptation

To identify the mutational pathways during adaptation to ethanol, weperformed whole-genome sequencing of populations of evolving cellsthroughout the two-year experiment. Each population sample was sequencedto 500 fold coverage on average, and this for multiple time points (34samples for all reactors combined, see FIG. 1). In addition to thiswhole-population sequencing strategy, we also sequenced for each timepoint the genomes of three adapted clones to 80 fold coverage onaverage, resulting in 102 clonal samples sequenced in total. Details onthe sequencing and analysis pipelines can be found in Materials andMethods.

Whole Genome Sequencing of Adapted Clones Reveals Mutators and ExtensiveAneuploidies

After 2 years, yielding around 200 generations, evolved clones(excluding clones from reactor 2 and 6, which acquired a mutatorphenotype, see below) contained on average 23 SNPs compared to theancestral strain (data not shown). This number is higher than what wouldbe expected based on measured rates of spontaneous mutations [50], andcould reflect an increased mutation rate under the stressful conditionsimposed by the high ethanol levels in our set-up. Across all reactorsand clones sequenced, we identified a total of 8932 different sitesmutated. The largest fraction are SNPs (6424 out of 8932; 72%); Indelsare found mostly in non-coding regions (1830 out of 2508; 73%), whereasSNPs are mostly found inside genes (4971 out of 6424; 77%). Most ofthese coding SNPs are non-synonymous (3672 out of 4971; 74%). In tworeactors (reactor 2 and reactor 6), we noticed a marked increase in thenumber of mutations found in individual clones (see FIG. 4, and data notshown). Specifically, clones from reactor 2 contain a significantlyhigher number of indels (about 929 on average per genome for clonesisolated after 200 generations, compared to 21 on average per genome forclones isolated after 200 generations in other reactors), whereas clonesfrom reactor 6 display an increase in the number of SNPs (about 1765 pergenome for clones isolated after 200 generations, compared to an averageof 31 per clone for other reactors). This high number of mutationspoints to a so-called “mutator” phenotype typically present in cellswith lower DNA replication fidelity and/or DNA repair. Interestingly,such mutators are frequently observed during evolution experiments,probably because mutators are more likely to acquire (combinations of)adaptive mutations faster [23,51-54]. Interestingly, the increasedmutation rate in reactor 2 is first observed around generation 70-80,coinciding with the appearance of a 21 bp insertion in the MSH2 gene—akey player in DNA mismatch repair. Mutations in MSH2 have beenpreviously reported to confer a mutator phenotype [55,56], and mutationsin MutS, the ortholog of MSH2 in E. coli, were identified in a long termevolution experiment and also result in a mutator phenotype [23].Indeed, deletion of this gene, as well as re-creating this insertion inan otherwise wild type background drastically increases mutation rate(data not shown). The MSH2 mutation eventually reaches a frequency of100% at the end of the evolution experiment (200 generations). Apartfrom the convergence towards diploidy (see above), we also detectedextensive copy number variation (CNVs) in our evolved clones, comprisingboth duplicated and deleted chromosomal regions (see FIG. 5 and data notshown). Other studies have observed similar copy number variation duringadaptation to stress, including heat stress and specific nutrientlimitations [24,28,33]. Interestingly, acquisition of an extra copy ofchromosome III appeared to be a common feature for most of our evolvedclones (data not shown). Some (parts of) other chromosomes, includingchromosome XII and chromosome IV, are also duplicated in several of ourevolved clones. These frequent occurrences indicate that these specificaneuploidies could be adaptive under ethanol conditions, although theexact mechanistic basis remains to be elucidated.

Whole-Genome Sequencing of Evolved Populations Reveals Extensive ClonalInterference

While the sequencing of clones yielded valuable information onindividual lineages within the evolving populations; sequencing ofevolving populations yielded more information on the complex mutationalpaths and dynamics between sub-populations within each evolvingpopulation. We observe distinct pattern of mutations appearing anddisappearing over time in each of the six reactors. Some of thesemutations remain in the population, eventually reaching high levels oreven complete fixation (i.e. presence in 100% of all cells in thepopulation). Other mutations only persist for a short time, untillineages carrying these mutations are outcompeted by others, so calledclonal interference. In total, we identified 1637 mutations across allpopulations and time points. 117 of these mutations are no longerpresent in the final time points sequenced, and 101 mutations drop morethan 10% in frequency after reaching their maximum frequency, indicativeof clonal interference. Interestingly, we also identified some overlapbetween the mutations found in different independently evolvingpopulations (i.e. in different reactors). Specifically, we find 20 genesthat are mutated twice in different generations and populations, 3 genesmutated 3 times, 2 genes mutated twice, 2 genes mutated 5 times and 1gene 6 times (data not shown). This significantly differs from whatwould be expected by chance (see Materials and Methods). Repeatedly hitgenes are, amongst others, involved in stress response, cell cycle andheme biosynthesis. The higher number of sequenced samples from reactors1 and 2 allowed us to further analyze these population sequences andgroup mutations based on correlations in the changes in their respectivefrequencies (based on the pipeline developed by [32]; see also Materialsand Methods). This yields a more detailed picture of the differentco-evolving sub-populations present in these reactors, which is depictedin the Muller diagrams of FIG. 6 (and data not shown).

In both reactors, selective sweeps mostly consists of groups ofmutations that move through the population together. While thesereactors were inoculated with the same strain, the type and dynamics ofmutations observed during adaptation appear very different. However,both evolving populations of reactor 1 and reactor 2 are characterizedby strong clonal interference. In reactor 1, 4 different subpopulationsare present around generation 90, each carrying different mutations. Bygeneration 130, these lineages have been outcompeted by another lineagethat almost completely dominates the population by 200 generations (datanot shown). In reactor 2, a lineage carrying a mutation in PDE2(encoding a high-affinity cAMP phosphodiesterase) is driven toextinction by a subpopulation carrying indels in ASG1 and MSH2. ASG1 isa transcriptional regulator involved in the stress response and has beenfound mutated in evolved populations from different reactors (includingreactor 1, see also FIG. 6, and data not shown). Another example ofclonal interference is observed in the later generations of thisreactor: a subpopulation carrying a mutation in CDC27 (encoding asubunit of the anaphase promoting complex/cyclosome) is driven toextinction by a subpopulation carrying mutations in BNI1 (important fornucleation of actin filaments) and PET123 (encoding a mitochondrialribosomal protein).

Diverse Pathways Involved in Adaptation to Ethanol

The results discussed above revealed extensive variability in the typeand number of mutations present in each evolving population. While thiscould suggest the presence of several, different mutational pathways(and thus lack of parallel evolution), mutations in different genesmight affect identical or similar pathways, implying that thephysiological adaptation to high ethanol might in fact be more similarthan what is immediately apparent from the individual mutations. To gaininsight into the affected biological pathways and investigate thepossible similarities in adaptation to increased ethanol, differentcomputational approaches were used. First, a term-enrichment analysiswas performed on the complete list of mutated genes for all reactors(for enriched clusters, see Table 1 and data not shown). Theseenrichment methods have been used as one of the standard functionalanalysis tools and gave us a first insight into potential adaptivepathways present in our evolved lineages.

TABLE 1 Top 10 functional clusters identified amongst genes hit bymutations in the evolution experiment. An enrichment score cutoff valueof 1.3, equivalent to a p-value of 0.05 for term enrichment was used[57]. Cluster ranking Cluster name Enrichment score 1 ATP-binding 3.54 2Cellular bud 2.46 3 Leucine-rich repeat 2.23 4 Mating projection 1.79 5DNA damage response 1.76 6 Phosphorylation 1.63 7 ABC transporters 1.608 Regulation of cell shape 1.47 9 tRNA aminoacylation 1.44 10Transcriptional regulation 1.36

In a second step, we used a sub-network-based selection method [58,59](see Materials and Methods) first developed for E. coli expression data.Here, we adapted and extended this method to select the subnetwork fromthe global yeast interaction network that best connected the mutatedgenes in the most parsimonious way. This method also identifies theintermediary genes involved in signaling mechanisms, which are notnecessarily mutated in our evolved lineages but mediate the cellularresponse. We excluded mutations obtained from the populations with amutator phenotype (reactor 2 and 6) because of their low signal to noiseratio. This analysis identifies genes frequently mutated in thedifferent populations (DSK2, ASG1), as well as genes that are closelyconnected on the interaction graph (HEM3, HEM12, . . . ). This latterset reflects parallelism at the pathway level in the different reactors.From FIG. 7, it is also clear that sometimes multiple mutationsoccurring in the same pathway originated in the same reactor rather thanacross different reactors. This could indicate not only a level ofparallelism between the different reactors but also between thesub-populations in the same reactor. To confirm this, we also appliedthe sub-network selection method on the mutations obtained for each ofthe populations separately. This confirms that within single populationsthe same pathways seem to be affected as those that are consistentlyaffected between the populations (including cell cycle, DNA repair andprotoporphyrinogen metabolism, see also below). All resultingsub-networks as well as the enriched genes (including p-values) can befound in FIG. 7 and data not shown. Additional analyses performed toincrease mechanistic insight, including interactome analyses of genescontaining non-synonymous mutations in our different population samplesas well as the effect of these mutations on protein functional domainswere also performed (data not shown).

Cell Cycle and DNA Replication

Our analyses suggest that the cell cycle and DNA replication areaffected in our evolved lineages (FIG. 7). Ethanol has been previouslyreported to delay cell cycle progression, probably throughdepolarization of the actin cytoskeleton [60]. This could be relevantfor adaptation in the sense that slower growth could protect cells fromstress, perhaps by inducing genes involved in the general stressresponse [61-63].

Respiration

PheNetic analysis shows that protoporphyrinogen metabolism is affectedin our evolved populations (FIG. 7). Since this is important for hemebiosynthesis, this could indicate that respiration is affecteddownstream of mutations. Non-synonymous SNPs in functional domains ormodified sites are expected to have more effect on the cell thanmutations outside of these regions. We find such mutations in proteinsinvolved in regulation and in enzymes of heme biosynthesis and proteinsthat need heme groups for proper functioning, that are themselvesinvolved in respiration (data not shown). These analyses point to a rolefor changes in respiration in adaptation to ethanol. Together, ourresults show evidence for parallel evolution, with the same processes(cell cycle and DNA replication) affected by different mutations, andalso highlight the plethora of processes involved in increased ethanoltolerance in our adapted lineages.

Example 2. Several High-Frequency Mutations Increase Ethanol Toleranceof Non-Evolved Strain

The high number of mutations (both SNPs and Indels) precluded anexhaustive analysis of all mutations present in our tolerant clones andpopulations and their effect on ethanol tolerance. One commonly usedapproach to investigate putative beneficial mutations is backcrossing ofevolved clones to their ancestor, which does not contain any mutation.However, since each of the evolved populations proved unable to form anyspores, this strategy was not accessible to us. Hence, we focused onSNPs reaching high frequencies in our 200 generation population samples.In total, nine SNPs were selected for further study (Table 2). Severalof the mutated genes belong to or are linked to the processes affectedacross and/or within specific reactors (heme metabolism, proteintransport and cell cycle, see also Table 2). These nine SNPs weresubsequently introduced into the ancestral haploid strain. The effect ofthese mutations was assessed by high-throughput competition experiments[64], in 0, 4, 6 and 8% (v/v) EtOH conditions, with glucose as a carbonsource (FIG. 8, data not shown). Several mutants show a clear increasein fitness, with the fitness effect often depending on the concentrationof ethanol in the medium. Most mutants show slightly increased fitnessin medium without added ethanol, with further increases in relativefitness with increasing exogeneous ethanol concentrations. Mutants inPRT1 and MEX67 are less fit than the WT in non-ethanol conditions, butshow increased fitness in higher ethanol levels. MEX67 is a poly (A) RNAbinding protein involved in nuclear mRNA export. PRT1 encodes the eIF3bsubunit of the eukaryotic translation initiation factor 3 (eIF3). Theincreased ethanol tolerance associated with these mutations hints attranslation processes as targets of ethanol. Interestingly, a recentstudy in E. coli showed that ethanol negatively impacts transcription aswell as translation [6]. However, if and how mutations in MEX67 and PRT1might mitigate this effect and contribute to ethanol tolerance remainsunknown.

TABLE 2 SNPs in evolved lineages selected for introduction innon-ethanol tolerant strain. SNPs reaching high frequencies in thedifferent evolved lineages were introduced in a haploid, non-ethanoltolerant strain. Gene functions were obtained from SGD,http://www.yeastgenome.org. Nucleotide Function of mutated Enrichedprocess Location SNP type change gene (SGD) involved in Reactor 1 ChrIV:1489310 Intergenic A > T NA HST4 Non- G262C Histone deacetylase, Cellcycle* synonymous involved in cell cycle progression, SCFA metabolismVPS70 Non- C595A Vacuolar protein Protein transport* synonymous sorting,unknown function Reactor 2 YBL059W Non- G479T Unknown function NAsynonymous PCA1 Non- C1583T P-type cation- Heme biosynthesis?*synonymous transporting ATPase (potential role in iron homeostasis)Reactor 3 ChrXII: 747403 Intergenic C > T NA HEM13 Non- G700C Hemebiosynthesis Protoporphyrinogen synonymous metabolism/heme biosynthesis*Reactor 5 PRT1 Non- A1384G elF3 subunit, essential NA synonymous forprotein synthesis Reactor 6 MEX67 Non- G456A Component of nuclear NAsynonymous pore, mRNA export *Enriched process identified by Phenticanalyses across all reactors

Of all mutations introduced into the ancestral strain, vps70C595Aprovided the largest fitness increase. VPS70 is putatively involved insorting of vacuolar carboxypeptidase Y to the vacuole [65]. A mutationin VPS70 has been recently identified by members of our team as adeterminant of ethanol tolerance in a Brazilian bioethanol strain [66].Interestingly, the VPS70 mutation in our evolved ethanol-tolerantlineages alters the same amino acid as the mutation present in theindustrial ethanol tolerant strain (Goovaerts A. and Thevelein J. M.,personal communication). The fact that one of the mutations identifiedin our evolved lineages was also found in an industrially usedbio-ethanol strain underscores the potential of our approach to findbiologically relevant mutations for increased ethanol tolerance. Othermembers of the VPS family have been previously implicated in ethanoltolerance as well, but also here the exact molecular mechanism throughwhich they could increase ethanol tolerance is still unclear [10,12,67].To our knowledge, none of the other genes investigated in this studyhave been previously implicated in ethanol tolerance nor have thesemutations been found in natural or industrial yeasts so far. Thisimplies that these mutations could be prime candidates to improve theethanol tolerance and production of existing industrial yeasts [68].

Example 3. Testing of Mutations in Industrial Reference Strain

As a next step, the 7 coding mutations shown in Table 2 (identified witha lab yeast strain) were introduced into the industrial referencebiofuel strain Ethanol Red, a diploid Saccharomyces cerevisiae strain.For each of the coding mutations, the two wild type alleles in EthanolRed were replaced with two mutant alleles (more details can be found inthe materials and methods section). Next, maximal ethanol accumulationin YP+35% (w/V) glucose was determined for each of these mutant strains.

The relative ethanol production of these mutants is summarised in FIG.9.

All but one of the mutants yielded higher ethanol titers. Statisticalsignificance was reached for the mex67 mutant. This mutation showed thehighest relative increase in the industrial strain. Of note, therelative increase in ethanol yield of around 4% is not 4% ABV extra, butan increase of 4% compared to the proper control. In practice, thiscorresponds to approximately 0.8% alcohol extra. All experiments wererepeated 4 times (real biological replicates, using independenttransformants where available).

Fermentation capacity in YP+35% glucose (w/v) of each of the mutants wascomparable to that of the wild type Ethanol Red strain. Fermentationcapacity of these mutants is summarised in FIG. 10.

Discussion

Many fundamental questions on the dynamics and genetics of adaptation toa complex and severe stress such as ethanol are still unanswered. Whichtype and number of mutations are needed and/or sufficient? To whatextent are these mutations and the pathways they affect predictable? Toaddress these questions, we performed high-coverage whole-genomesequencing of clones and populations isolated throughout a two-yearevolution experiment and characterized their adaptation to increasingethanol levels. This allowed us to paint a detailed picture of themutational dynamics in our evolving lineages.

Our study demonstrates how many different evolutionary mechanisms allcome together to provide adaptation to a severe and complex stress.Specifically, we find that adaptation to high ethanol levels involveschanges in ploidy, copy number variation and the appearance of mutatorphenotypes, with evolving populations showing strong clonalinterference. Although these mechanisms have been observed in otherstudies (see, for example, [24,27,32,69-71], different mechanisms areoften observed and/or studied separately. More importantly, intraditional evolution studies, populations are exposed to a fixedconcentration of antibiotic or limiting nutrient. Under theseconditions, selection pressure is reduced or even eliminated when cellsbecome resistant. In our experimental set-up, ethanol levels werestepwise increased over time so that cells were evolving under constantselection. High concentrations of ethanol also actively killnon-tolerant cells, so that cells not adapted to the higher ethanolconcentrations die and/or are washed out of the turbidostat. Takentogether, our study combines aspects of traditional evolution studieswith principles used in morbidostat experiments [27]. Under such severestress conditions (near-morbidostats), the number of generations may notbe the ultimate way of measuring evolutionary time: cell death andmutations that are not associated with DNA replication becomeincreasingly important, which could result in quick sweeps.

We find extensive variation in genome size in our evolved cells; withaneuploidies in a large number of our evolved clones as well asconvergence toward a diploid state in our initially haploid andtetraploid populations. Becoming diploid thus appears to be a frequentlyused strategy by cells of different ploidy which effectively increasesethanol tolerance. Convergence to diploidy has been observed in otherlaboratory evolution experiments [25,36,72]. In contrast to thesestudies, we could demonstrate a clear fitness advantage of our diploidstrains in ethanol. Although further research is needed to fullyunderstand this fitness advantage, changes in gene expression and/orcell size could be important factors contributing to the increasedfitness of diploid cells [41,73,74]. Unfortunately, this convergentevolution prevented us from performing a detailed analysis on thedifference in adaptive strategies employed by haploids, diploids andtetraploids to increase ethanol tolerance.

Aneuploidy and copy-number variation are increasingly recognized ascommon themes in rapid adaptation [24,28,69,70]. It is believed thatchanges in the copy number of chromosomes or chromosomal fragmentsprovide a relatively easily accessible way to change expression levelsof specific key genes [75,76], and these CNVs can provide large, usuallycondition-specific, fitness effects [77]. However, such large-scalechanges in the genome likely also have some unwanted, detrimental sideeffects, such as imbalance between gene products and genomedestabilization [78-80]. Evolving populations are believed to graduallyreplace adaptive CNVs with more specific mutations that show fewerpleiotropic effects [28]. Notably, several of our evolved clones,isolated from different reactors, carry an extra copy of chromosome IIIand/or ChrXII; pointing towards a potential adaptive benefit of thisspecific aneuploidy. Interestingly, we find that clones isolated atlater time points have a specific, smaller region of chromosome XIIamplified (data not shown); indicating a more refined solution. GOenrichment and network analyses of the repeatedly amplified region ofChrXII (position 657500-818000) hints at cell wall formation as one ofthe key processes affected by these amplifications. Previous studieshave indeed shown that cell wall stability is a key factor involved inethanol tolerance [18].

Apart from diploidization of our evolving lineages, another example ofparallelism at the phenotypic level is the appearance of a mutatorphenotype in two of our six evolving populations. The sweep of the MSH2mutation in reactor 2 is likely caused by a so-called hitchhiking event,with the high mutation rates in the MSH2 mutant leading to theappearance of one or several beneficial mutations that drive theselective sweep. Because of the lower temporal resolution of sequencedpopulation samples of reactor 6, identifying the allele(s) underlyingthe mutator phenotype in this reactor has proven to be difficult.Parallelism at the genotypic level is less clear: we find few mutationsand mutated genes shared between the different evolved lineages.Applying different types of network and enrichment analyses revealedfunctional modules affected in several of the adapted populations. Thesepathways include response to stress, intracellular signal transduction,cell cycle and pathways related to membrane composition and organization(such as isoprenoid metabolism, glycerophospholipid catabolism andfatty-acyl-coA metabolism). For some of these pathways, further work isneeded to clarify their exact involvement in ethanol tolerance.

To investigate the phenotypic effect of mutations present in our evolvedlineages, we performed high-throughput fitness measurements in differentethanol concentrations. While our evolved lineages contained multiplemutations, single mutations reaching high frequency in the evolvedpopulations could already significantly increase ethanol tolerance whenintroduced into the ancestral, non-evolved haploid strain. Moreover,several of these single mutations selected for further study also showeda (modest) fitness benefit in conditions with no ethanol, with greaterincreases as ethanol levels rise. The number of mutations identified inthis study was too large to investigate the fitness effect of eachindividual mutation.

However, our strategy to select mutations that reached fixation in theevolved populations clearly proved successful, identifying as many as 7mutations (out of 9 tested) that confer a fitness advantage in highethanol environments. Mutations in genes linked to processes identifiedas affected across our different reactors—protein transport (VPS70) andheme metabolism (HEM13 and PCA1)—indeed increased fitness in EtOH. Thisunderscores the potential of PheNetic, a sub-network based selectionmethod for identifying adaptive mutations. Two of the mutations tested,ybl059wG479T and hst4G262C, did not increase fitness, although theyreached high frequency in our adapted populations. This is indicative ofhitch-hiking with other, beneficial mutations; or possible epistaticinteractions with other mutations.

Why then would not all feral yeasts show high ethanol tolerance, if itappears so easy to attain? Firstly, it seems plausible that not allyeasts are confronted with selection for high ethanol tolerance.Furthermore, it is important to note that we have not tested the fitnessof the mutants under many different conditions that mimic the naturalhabitats of yeasts. It seems likely that some of the mutationsidentified in this study would result in lower fitness in otherenvironments [81,82]. Moreover, we have not investigated the effect ofcombined mutations. While it is possible that combining differentmutations could increase ethanol tolerance even further, it also seemslikely that some mutations and/or specific combinations of mutationscould lead to reduced fitness in different low and/or high ethanolenvironments. Indeed, while our clones have increased fitness in EtOH,we observe that fitness of several of our evolved clones (containingmultiple other mutations apart from the ones investigated in this study)decreased in medium without exogenous EtOH (data not shown). Theseresults are indicative of antagonistic pleiotropy: the specificmutations present in our evolved clones increase fitness in onecondition (high ethanol, which was selected for), whereas they reducefitness in other environments. Ethanol resistance is an important traitfor the survival of feral yeasts in nature because the ethanol producedinhibits growth of competing microorganisms, while it serves as a carbonsource in later stages of growth, when all fermentable sugars aredepleted (the so-called make accumulate-consume strategy [83-85]). Ourresults suggest that adaptation to high ethanol is complex and can bereached through different mutational pathways. Apart from yieldinginsight into the evolutionary mechanisms leading to such complex andecologically important phenotypes, our study is also of considerableindustrial importance. Several of the mutations identified in this studymay be useful to increase the ethanol tolerance of industrial strainsused for the production of alcoholic beverages or biofuels.

Materials and Methods

Strains Used in this Study

Starting strains for the evolution experiment are all derived from thehaploid prototrophic S288cstrain FY5 [86]. To prevent clumping of cellsduring the evolution experiment, the flocculation genes FLO1, FLO10 andFLO11 were deleted in this strain using deletion cassettes based onpUG6, conferring resistance to G-418 disulfate [87]. Markers wereremoved through the Cre/LoxP technique using pSH65 [88]. Mating typeswitching of this strain was then performed, using plasmid pSB283, tocreate isogenic diploid and tetraploid strains. Fluorescent versions ofstrains (YECitrine or mCherry tagged) were constructed by integratingfluorescent markers at an intergenic, neutral region of chromosome II.

Long-Term Selection

Populations were founded in 400 mL ethanol containing media. Mediacontained 10 g/L yeast extract, 20 g/L bactopeptone, 4% (w/v) glucose,0.001% (v/v) Rhodorsil, Antifoam Silicone 46R, chloramphenicol (50μg/mL) and increasing concentrations of ethanol. Populations weremaintained at an average population size of 1010 cells. After 25generations, the level of EtOH in the media was increased each time(starting at 6% (v/v) and reaching 12% at 200 generations).

Turbidostat cultures were maintained using Sixfors reactors (Infors) at30° C., pH was kept constant at 5.0 with continuous mixing at 250 rpm inaerobic conditions. At regular times, a population sample was obtainedfrom each of the cultures for further analyses and stored in glycerol at−80° C. For DNA extraction purposes, a population cell pellet was alsofrozen down at −80° C.

Fitness Determination

Fitness for all evolved strains was determined in rich medium (YP, 2%(w/v) glucose) with 9% (v/v) ethanol, by competing strains against aYECitrine labeled ancestral strain. Cultures were pre-grown in YPD 6%ethanol. After 12h, wells of a 96 deep well plate were inoculated withequal numbers of labeled reference and unlabeled strains (˜106 cells ofeach) and allowed to grow for around 10 generations. Outer wells onlycontained medium and acted as a buffer to prevent ethanol evaporation.Additionally, plates were closed with an adhesive seal and plastic lid,and parafilm was used to prevent ethanol evaporation. Cultures wereregularly transferred to new medium to prevent nutrient depletion. Theratio of the two competitors was quantified at the initial and finaltime points by flow cytometry. Data analysis was done in FlowJo version10. Measurements were corrected for the small percentage of labeled,non-fluorescent cells that occurred even when the reference strain wascultured separately as well as for the cost of YFP expression in thelabeled reference strain. For each fitness measurement, threeindependent replicates were performed. The selective advantage, s, ofeach strain was calculated as s=(ln(Uf/Rf)−ln(Ui/Ri))/T where U and Rare the numbers of unlabeled and reference strain respectively, thesubscripts refer to final and initial populations and T is the number ofgenerations that reference cells have proliferated during thecompetition. The fitness of the unlabeled WT strain was designated 1,fitness of the evolved strains as 1+s.

Determination of Cell Ploidy

DNA content of evolved populations and evolved clones was determined bystaining cells with propidiumiodide (PI) and analyzing 50 000 cells byflow cytometry on a BD Influx. The ancestral haploid and diploid strainsused in the evolution experiment were used for calibration.

Whole Genome Sequencing

For evolved populations, genomic DNA was directly extracted from pelletsthat were frozen at the time of sample taking. Evolved clones wereselected from the different population samples by streaking glycerolstocks from the corresponding population samples on YPD plates. Swabsfrom each population were subsequently grown in YPD 6% EtOH anddilutions were plated on YPD plates with different ethanolconcentrations (ranging from 8% to 10%; with a 0.5% stepwise increase inethanol concentrations). From these plates, ethanol tolerant clones wereselected and genomic DNA of these clones was extracted. Genomic DNA wasprepared using the Qiagen genomic tip kit. Final DNA concentrations weremeasured using Qubit. Paired-end sequencing libraries with a mean insertsize of 500 bp were prepared and libraries were run on an IlluminaHiSeq2000 (EMBL GeneCore facility, Heidelberg). Average sequencingcoverage for clone and population sample is 80× and 500× respectively.

Variant Calling, Filtration and Annotation

Adaptors removal and reads quality control were done by Trim Galore!(http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) withoptions (−q 30 −length 50). The clean reads were then mapped referenceS. cerevisiae genome (S288C, version genebank64) using Burrows WheelerAlignment allowing maximum 50 bp gaps [89]. To identify mutations weemployed the BROAD Institute Genome Analysis Toolkit 628 (GATK, version3.1) [90]. We began by performing local realignment of reads aroundIndels, in order to eliminate false positives due to misalignment ofreads, which was followed by a base recalibration step. We thenperformed SNP and Indel calling according to the GATK best practicerecommendations. Population samples and single clone datasets wereanalyzed independently. For population data, Indel calling was performedusing the UnifiedGenotyper tool, and the identified variants werefiltered based on the recommended criteria (QD<2.0, FS>200.0,ReadPosRankSum<−20 and InbreedingCoef<−0.8). For the SNPs, we used theUnifiedGenotyper to perform multi-sample SNP calling on all thepopulation samples together. The resulting multi-sample callset was thenused as a prior to call SNPs in all individual samples. The called SNPswere then subject to filtering (QD<2.0, FS>60, ReadPosRankSum<−8.0,MQ<40, MQRankSum<−12.5). Furthermore, we filtered out SNPs that werecalled inside regions containing Indels. We also generated a list of allknown repetitive regions and low complexity regions using the programRepeatMasker (http://www.repeatmasker.org/cgibin/WEBRepeatMasker), whichwere masked out from the assemblies. Finally, variants present in theancestral strain were filtered out from all samples. A similar approachwas used to analyze the clonal samples. Ploidy level of each isolatedclone was determined by PI staining and variant calling was performedwith UnifiedGenotyper for haploid clones or HaplotypeCaller for diploidclones. Subsequently, SNPs and Indels were filtered based on the GATKbest practice recommendations as mentioned above. The inbreedingcoefficient filter was excluded in the clonal sample filtering forIndels, as it is a population-level metric. Annotation and effectprediction of all identified variants was performed using snpEff [91].Final processing of the data was performed using a R script, whichinvolved filtering out variants called within the sub-telomeric regions(15 kbps from the chromosome ends) and—for population samples—variantswhose frequency did not change by more than 10% during the experiment.We used the infrastructure of the VSC—Flemish Supercomputer Center forthese analyses. For improved performance, CPU multithreading (−nct)capabilities of GATK were utilized, providing up to 20 cores and maximummemory capacity of 64 GB per process.

Identification of Copy Number Variations (CNV)

CNVs in the samples were identified using Nexus Copy Number software,version 7.5 (http://www.biodiscovery.com/software/nexus-copy-nurnber).Reads were accumulated in 500-base bins along all chromosomes, rejectingbins with less than 10 reads. Log 2 ratios of copy numbers were thenestimated from the read-depth counts in these intervals. We used thefollowing calling parameters: minimum number of probes per segment of 5,significance threshold of p-value=1×10̂-9, percentage of removed outliersof 5% and the limits for copy gain and loss of +0.25 and −0.25,respectively.

Statistical Analyses of Genes Hit Multiple Times

The p-values probabilities of genes being hit by a mutation a specificnumber of times were calculated using the binomial distribution. Thenumber of draws was set to the total number of mutations found insidecoding regions (i.e. 817); the number of successes was set to the numberof times a specific gene was hit by a mutation (2 to 6); the probabilityof success was set to the specific ratio of the length of each gene hitmultiple times (in nucleotides) over the entire coding content of the S.cerevisiae genome (9080922 nucleotides).

Haplotype Reconstruction

Haplotype reconstruction was based on the approach described by [32].Prior to the actual reconstruction, variants identified in thesequencing data were subject to further processing by excluding variantsthat were multi-allelic, variants that exhibited mixed zygosity in theisolated clones (i.e. homozygous in one clone and heterozygous inanother clone) and variants that didn't reach the frequency of 0.2 atany point during the experiment. Haplotypes (or ‘mutational cohorts’)present in the remaining variants were next reconstructed using a Matlabscript (kindly provided by the Desai lab) on a local machine runningMatlab 2013a. Briefly, variants present in our dataset were clusteredinto haplotypes based on the Euclidean distance between theirfrequencies at specific time-points of the experiment. Afterward,frequencies of individual variants assigned to a specific haplotype wereaveraged to obtain the frequency of the haplotype itself. Frequencies ofthe identified haplotypes at specific time points (data not shown) werethen used as source data to draw their approximate Muller diagramrepresentations with lnkscape 0.48.4.

Enrichment and Network Analysis

Functionally meaningful terms enriched in the list of genes hit in ourevolution experiment were identified using DAVID Tools [57,92]. Overall,we identified 170 clusters of functionally meaningful terms, of which 10passed our statistical enrichment criteria. An enrichment score cutoffvalue of 1.3 was used, equivalent to a p-value of 0.05 for termenrichment, as recommended by the authors [57].

Phenetic Analyses

Intergenic mutations were discarded for the analysis. The interactionnetwork used as input for PheNetic was composed of interactomics dataobtained from KEGG [93] for metabolic interactions, String forprotein-protein interactions [94] and Yeastract for protein-DNAinteractions [95]. The total interaction network contains 6592 genes and135266 interactions. This interaction network was converted to aprobabilistic network using the distribution of the out-degrees of theterminal nodes of the network edges. By doing so, edges connecting nodeswith a low out-degree will receive a high probability while edgesconnecting nodes with a high out-degree receive a low probability. Usinglists of mutated genes as input, PheNetic will now infer thatsub-network of the probabilistic network that best connects the mutatedgenes in the list over the probabilistic network. As the probabilisticnetwork penalizes hub nodes, the inferred sub-network will thereforepreferentially connect the mutated genes through the least connectedparts of the network. This results in selecting the most specific partsof the network that can be associated with the mutated genes.

PheNetic was used to connect the mutated genes over the interactionnetwork [58] with the following parameters: 100-best paths with amaximum path length of 4 were sampled between the different mutations incombination with a search tree cutoff of 0.01. As the size of theselected sub-network by PheNetic is dependent on both a cost parameterand the number of mutated genes in the input, different costs were usedfor the sub-network inference from different sizes of mutated genelists. For the sub-network inference between all the mutated genes fromthe non-mutator reactors a cost of 0.25 was used, for the non-mutatorreactors (1,3,4,5) a cost of 0.05 was used as they all have a similaramount of mutated genes, and for the mutator reactors (2 and 6) a costof 0.5 was used.

Network Visualization and Enrichment

The resulting networks were visualized using Cytoscape and a functionalenrichment using the biological process terms of Gene Ontology incombination with the annotation of SGD of the sub-networks was performedusing the Bingo plugin, version 2.44 [96].

Construction of Mutant Strains

Selected mutations identified from the whole-genome sequencing data wereintroduced into the ancestral genetic background using the followingprotocol. First, a selectable marker conferring resistance tohygromycine was introduced near the genomic location of interest throughhomologous recombination. Part of this locus was then amplified togetherwith the selectable marker using a forward primer containing the desiredmutation. The resulting PCR product was then transformed into theancestral strain; and presence of the mutation was verified by Sangersequencing.

High-Throughput Competitive Fitness Measurements

YECitrine- or mCherry-tagged site-directed mutant strains were competedwith the parental mCherry or YECitrine tagged strains, respectively, asdescribed [64]. In brief, saturated cultures of mutant and parentalstrains were mixed in equivalent volumes and inoculated onto 150 μl ofYNB-low fluorescent medium in 96-well microtiter plates (Corning 3585).Micro-cultures grew without shaking and were serial-diluted every 24 hrsfor approximately 28 generations (7 days) in a fully automated roboticsystem (Tecan Freedom EVO200) that integrates a plate carrousel (LiconicSTX110), a plate reader (Tecan Infinite M1000), a 96-channel pipettinghead, an orbital plate shaker, and a robotic manipulator arm. Theequipment was maintained in an environmental room at constanttemperature (30° C.) and relative humidity (70%). Fluorescence signal(mCherry: Ex 587 nm/5 nm and Em 610 nm/5 nm; YECitrine: Ex 514 nm/5 nmand Em 529 nm/5 nm) and absorbance at 600 nm were monitored every hourduring the entire experiment. The YECitrine- or mCherry-tagged parentalstrains were competed to each other for normalization and monitoredindividually to determine background fluorescence signal. Fluorescenceand absorbance output data was analyzed in Matlab as described [64] toobtain an average selection coefficient, smut, with its S.E.M. fromthree experimental replicates.

Introduction of Selected Alleles in Ethanol Red

Selected mutations identified from the whole-genome sequencing data wereintroduced into the diploid strain Ethanol Red using the followingprotocol. First, a selectable marker conferring resistance to hygromycinwas introduced near the genomic location of interest through homologousrecombination. Part of this locus was then amplified together with theselectable marker using a primer containing the desired mutation. Theresulting PCR product was then transformed into Ethanol Red; andpresence of the mutation was first identified by PCR using 3′ mismatchprimer pairs, and subsequently verified by Sanger sequencing. Second, aselectable marker conferring resistance to nourseothricin was introducedinto Ethanol Red near the genomic location of interest. Part of thislocus with the desired mutation was amplified together with the marker.The PCR product was then transformed into the corresponding mutants,which already has one copy of the target gene mutated and linked tohygromycin resistance marker. The selection of successful transformantswas performed on double selection, and the mutants were first identifiedby PCR, then verified by Sanger sequencing.

Protocol Very-High Gravity (VHG) Fermentations

Lab-scale fermentations under VHG conditions were started with anovernight pre-growth of a single colony into 3 ml YPD, followed bytransfer of the entire culture to 30 ml YP+4% (w/v) glucose andadditional growth for 48 h to the stationary phase (200 rpm, 30° C.);250-ml Schott bottles each filled with 150 ml YP+35% (w/v) glucose and amagnetic rod (35×5 mm) were inoculated to a starting OD₆₀₀=1.0(approximately 2.0×10⁷ cells/ml). These bottles were sealed with awaterlock and stirred continuously at 150 rpm on a magnetic stirringplatform (IKA® RO 15) at 30° C. The bottles were weighed daily todetermine the cumulative weight loss, a proxy for CO₂ production andfermentations were stopped after 7 days. The ethanol production wasdetermined using Anton Paar Alcolyzer. A Tukey HSD test was performed tocheck for significant differences in ethanol production between mutantsand their respective controls.

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1.-11. (canceled)
 12. A method of increasing alcohol tolerance and/oralcohol accumulation in yeast, the method comprising: utilizing an MEX67allele in the yeast so as to increase the yeast's alcohol toleranceand/or alcohol accumulation.
 13. The method according to claim 12,wherein the MEX67 allele is combined with other alcohol toleranceallele(s) and/or accumulation modulating allele(s).
 14. The methodaccording to claim 13, wherein the other alcohol tolerance allele(s)and/or accumulation modulating allele(s) is/are selected from the groupconsisting of PCA1, PRT1, YBL059W, HEM13, HST4, and VPS70.
 15. Themethod according to claim 12, wherein the MEX67 allele has a G to Amutation at a position relative to position 456 of SEQ ID NO:1.
 16. Themethod according to claim 13, wherein the other alcohol toleranceallele(s) and/or accumulation modulating allele(s) are selected from thegroup consisting of PCA1 with a C to T mutation at a position relativeto position 1583 of SEQ ID NO:2, PRT1 with a A to G mutation at aposition relative to position 1384 of SEQ ID NO:3, YBL059W with a G to Tmutation at a position relative to position 479 of SEQ ID NO:4, HEM13with a G to C mutation at a position relative to position 700 of SEQ IDNO:5, HST4 with a G to C mutation at a position relative to position 262of SEQ ID NO:6, VPS70 with a C to A mutation at a position relative toposition 595 of SEQ ID NO:7, the intergenic region of Chromosome IV withan A>T substitution at position 1489310, and the intergenic region ofChromosome XII with a C>T substitution at position
 747403. 17. Themethod according to claim 15, wherein the mutation in the MEX67 alleleis a G456A mutation.
 18. The method according to claim 16, wherein theother alcohol tolerance allele(s) and/or accumulation modulatingallele(s) are selected from the group consisting PCA1 with a C1583Tmutation, PRT1 with a A1384G mutation, YBL059W with a G479T mutation,HEM13 with a G700C mutation, HST4 with a G262C mutation, and VPS70 witha C595A mutation.
 19. The method according to claim 12, wherein thealcohol is ethanol.
 20. The method according to claim 12, wherein theyeast is a Saccharomyces spp.
 21. A process for producing a yeast strainwith greater alcohol accumulation and/or resistance than a wild-type ofthe yeast strain, the method comprising: incorporating an MEX67 alleleinto the yeast strain so as to increase the yeast's alcohol toleranceand/or alcohol accumulation.
 22. The process of claim 21, furthercomprising: incorporating into the yeast strain other alcohol toleranceallele(s) and/or accumulation modulating allele(s).
 23. The process ofclaim 22, wherein the other alcohol tolerance allele(s) and/oraccumulation modulating allele(s) is/are selected from the groupconsisting of PCA1, PRT1, YBL059W, HEM13, HST4, and VPS70.
 24. Theprocess of claim 22, wherein the MEX67 allele has a G to A mutation at aposition relative to position 456 of SEQ ID NO:1.
 25. The process ofclaim 23, wherein the other alcohol tolerance allele(s) and/oraccumulation modulating allele(s) are selected from the group consistingof PCA1 with a C to T mutation at a position relative to position 1583of SEQ ID NO:2, PRT1 with a A to G mutation at a position relative toposition 1384 of SEQ ID NO:3, YBL059W with a G to T mutation at aposition relative to position 479 of SEQ ID NO:4, HEM13 with a G to Cmutation at a position relative to position 700 of SEQ ID NO:5, HST4with a G to C mutation at a position relative to position 262 of SEQ IDNO:6, VPS70 with a C to A mutation at a position relative to position595 of SEQ ID NO:7, the intergenic region of Chromosome IV with an A>Tsubstitution at position 1489310, and the intergenic region ofChromosome XII with a C>T substitution at position
 747403. 26. Theprocess of claim 25, wherein the mutation in the MEX67 allele is a G456Amutation.
 27. The process of claim 25, wherein the other alcoholtolerance allele(s) and/or accumulation modulating allele(s) areselected from the group consisting PCA1 with a C1583T mutation, PRT1with a A1384G mutation, YBL059W with a G479T mutation, HEM13 with aG700C mutation, HST4 with a G262C mutation, and VPS70 with a C595Amutation.
 28. The process of claim 23, wherein the yeast strain is aSaccharomyces spp.
 29. A genetically modified Saccharomyces spp.comprising: at least one allele selected from the group consisting of anMEX67 allele with a G456A mutation, a PCA1 allele with a C1583Tmutation, a PRT1 allele with a A1384G mutation, a YBL059W allele with aG479T mutation, a HEM13 allele with a G700C mutation, a HST4 allele witha G262C mutation, a VPS70 allele with a C595A mutation, the intergenicregion of Chromosome IV with an A>T substitution at position 1489310,and the intergenic region of Chromosome XII with a C>T substitution atposition 747403.